Reducing Interruptive Alert Burden Using Quality Improvement Methodology

Factors Influencing Medication Administration Outcomes Among New Graduate Nurses Using Bar Code-Assisted Medication Administration

Paramount to patient safety is the ability for nurses to make clinical decisions free from human error. Yet, the dynamic clinical environment in which nurses work is characterized by uncertainty, urgency, and high consequence, necessitating that nurses make quick and critical decisions. The aim of this study was to examine the influence of human and environmental factors on the decision to administer among new graduate nurses in response to alert generation during bar code-assisted medication administration. The design for this study was a descriptive, longitudinal, observational cohort design using EHR audit log and administrative data. The study was set at a large, urban medical center in the United States and included 132 new graduate nurses who worked on adult, inpatient units. Research variables included human and environmental factors. Data analysis included descriptive and inferential analyses. This study found that participants continued with administration of a medication in 90.75% of alert encounters. When considering the response to an alert, residency cohort, alert category, and previous exposure variables were associated with the decision to proceed with administration. It is important to continue to study factors that influence nurses' decision-making, particularly during the process of medication administration, to improve patient safety and outcomes.

Randomized Comparison of Electronic Health Record Alert Types in Eliciting Responses about Prognosis in Gynecologic Oncology Patients

OBJECTIVES

 To compare the ability of different electronic health record alert types to elicit responses from users caring for cancer patients benefiting from goals of care (GOC) conversations.

METHODS

 A validated question asking if the user would be surprised by the patient's 6-month mortality was built as an Epic BestPractice Advisory (BPA) alert in three versions-(1) Required on Open chart (pop-up BPA), (2) Required on Close chart (navigator BPA), and (3) Optional Persistent (Storyboard BPA)-randomized using patient medical record number. Meaningful responses were defined as "Yes" or "No," rather than deferral. Data were extracted over 6 months.

RESULTS

 Alerts appeared for 685 patients during 1,786 outpatient encounters. Measuring encounters where a meaningful response was elicited, rates were highest for Required on Open (94.8% of encounters), compared with Required on Close (90.1%) and Optional Persistent (19.7%) (p < 0.001). Measuring individual alerts to which responses were given, they were most likely meaningful with Optional Persistent (98.3% of responses) and least likely with Required on Open (68.0%) (p < 0.001). Responses of "No," suggesting poor prognosis and prompting GOC, were more likely with Optional Persistent (13.6%) and Required on Open (10.3%) than with Required on Close (7.0%) (p = 0.028).

CONCLUSION

 Required alerts had response rates almost five times higher than optional alerts. Timing of alerts affects rates of meaningful responses and possibly the response itself. The alert with the most meaningful responses was also associated with the most interruptions and deferral responses. Considering tradeoffs in these metrics is important in designing clinical decision support to maximize success.

Choice Architecture in Opioid Safety Alerting

The need for effective and efficient clinical decision support (CDS) embedded in electronic health record (EHR) processes is growing. Using choice architecture design strategies may increase effectiveness of CDS solutions. The authors describe implementation of an opioid risk alert and subsequent revisions of that alert to increase effectiveness and reduce alert volumes. The first version of the alert used an opt-in choice architecture when recommending naloxone and the second version used an active choice design. The percentage of opioid prescriptions ordered with naloxone prescribed within the last 12 months increased significantly after implementation of the first version of the alert and then further increased significantly after implementation of the second version. Alert volumes decreased over the same timeframe. An education campaign was also implemented during the timeframe studied and likely also contributed to the naloxone outcomes seen.

Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support

BACKGROUND

Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas.

OBJECTIVES

Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders.

METHODS

Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts.

RESULTS

At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes.

CONCLUSION

CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Post-implementation optimization of medication alerts in hospital computerized provider order entry systems: a scoping review

OBJECTIVES

A scoping review identified interventions for optimizing hospital medication alerts post-implementation, and characterized the methods used, the populations studied, and any effects of optimization.

MATERIALS AND METHODS

A structured search was undertaken in the MEDLINE and Embase databases, from inception to August 2023. Articles providing sufficient information to determine whether an intervention was conducted to optimize alerts were included in the analysis. Snowball analysis was conducted to identify additional studies.

RESULTS

Sixteen studies were identified. Most were based in the United States and used a wide range of clinical software. Many studies used inpatient cohorts and conducted more than one intervention during the trial period. Alert types studied included drug-drug interactions, drug dosage alerts, and drug allergy alerts. Six types of interventions were identified: alert inactivation, alert severity reclassification, information provision, use of contextual information, threshold adjustment, and encounter suppression. The majority of interventions decreased alert quantity and enhanced alert acceptance. Alert quantity decreased with alert inactivation by 1%-25.3%, and with alert severity reclassification by 1%-16.5% in 6 of 7 studies. Alert severity reclassification increased alert acceptance by 4.2%-50.2% and was associated with a 100% acceptance rate for high-severity alerts when implemented. Clinical errors reported in 4 studies were seen to remain stable or decrease.

DISCUSSION

Post-implementation medication optimization interventions have positive effects for clinicians when applied in a variety of settings. Less well reported are the impacts of these interventions on the clinical care of patients, and how endpoints such as alert quantity contribute to changes in clinician and pharmacist perceptions of alert fatigue.

CONCLUSION

Well conducted alert optimization can reduce alert fatigue by reducing overall alert quantity, improving clinical acceptance, and enhancing clinical utility.

Reducing low-value ED coags across 11 hospitals in a safety net setting

BACKGROUND

Prothrombin/international normalized ratio and activated partial thromboplastin time (PT/INR and aPTT) are frequently ordered in emergency departments (EDs), but rarely affect management. They offer limited utility outside of select indications. Several quality improvement initiatives have shown reduction in ED use of PT/INR and aPTT using multifaceted interventions in well-resourced settings. Successful reduction of these low-value tests has not yet been shown using a single intervention across a large hospital system in a safety net setting. This study aims to determine if an intervention of two BPAs is associated with a reduction in PT/INR and aPTT usage across a large safety net system.

METHODS

This initiative was set at a large safety net system in the United States with 11 acute care hospitals. Two Best Practice Advisories (BPAs) discouraging inappropriate PT/INR and aPTT use were implemented from March 16, 2022-August 30, 2022. Order rate per 100 ED patients during the pre-intervention period was compared to the post-intervention period on both the system and individual hospital level. Complete blood count (CBC) testing served as a control, and packed red blood cell transfusions served as a balancing measure. An interrupted time series regression analysis was performed to capture immediate and temporal changes in ordering for all tests in the pre and post-intervention periods.

RESULTS

PT/INR tests exhibited an absolute decline of 4.11 tests per 100 ED encounters (95% confidence interval -5.17 to -3.05; relative reduction of 18.9%). aPTT tests exhibited absolute decline of 4.03 tests per 100 ED encounters (95% CI -5.10 to -2.97; relative reduction of 19.8%). The control measure, CBC, did not significantly change (-0.43, 95% CI -2.83 to 1.96). Individual hospitals showed variable response, with absolute reductions from 2.02 to 9.6 tests per 100 ED encounters for PT/INR (relative reduction 12.1%-30.5%) and 2.07 to 10.04 for aPTT (relative reduction 12.1%-31.4%). Regression analysis showed that the intervention caused an immediate 25.7% decline in PT/INR and 24.7% decline in aPTT tests compared to the control measure. The slope differences (rate of order increase pre vs post intervention) did not significantly decline compared to the control.

CONCLUSIONS

This BPA intervention reduced PT/INR and aPTT use across 11 EDs in a large, urban, safety net system. Further study is needed in implementation to other non-safety net settings.

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore

Objective

Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes.

Materials and Methods

This retrospective single-center study was done at Jurong Health Campus. Aggregated, anonymized data on patient demographics and alert statistics were collected from January 1, 2018 to December 31, 2021. "Preintervention" period was January 1-December 31, 2018, and "postintervention" period was January 1-December 31, 2021. The intervention period was the intervening period. Categorical variables were reported as frequencies and proportions and compared using the chi-square test. Continuous data were reported as median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test. Statistical significance was defined at P < .05.

Results

There was a significant reduction of 59.6% in the total number of interruptive BPA alerts, despite an increase in the number of unique BPAs from 54 to 360 from pre- to postintervention. There was a 74% reduction in the number of alerts from the 7 BPAs that were optimized from the pre- to postintervention period. There was a significant increase in percentage of overall interruptive BPA alerts with action taken (8 [IQR 7.7-8.4] to 54.7 [IQR 52.5-58.9], P-value < .05) and optimized BPAs with action taken (32.6 [IQR 32.3-32.9] to 72.6 [IQR 64.3-73.4], P-value < .05). We estimate that the reduction in alerts saved 3600 h of providers' time per year.

Conclusions

A significant reduction in interruptive alert volume, and a significant increase in action taken rates despite manifold increase in the number of unique BPAs could be achieved through concentrated efforts focusing on governance, data review, and visualization using a system-embedded tool, combined with the CDS Five Rights framework, to optimize alerts. Improved alert compliance was likely multifactorial-due to decreased repeated alert firing for the same patient; better awareness due to stakeholders' involvement; and less fatigue since unnecessary alerts were removed. Future studies should prospectively focus on patients' clinical chart reviews to assess downstream effects of various actions taken, identify any possibility of harm, and collect end-user feedback regarding the utility of alerts.

Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study

BACKGROUND

Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes.

OBJECTIVE

This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care.

METHODS

A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team.

RESULTS

Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups.

CONCLUSIONS

Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.

Paediatric clinical decision support: Evaluation of a best practice alert for red blood cell transfusion

BACKGROUND AND OBJECTIVES

Providing red blood cell (RBC) transfusion to paediatric patients with a haemoglobin (Hb) level of <7 g/dL is the current best practice, but it is often difficult to ensure appropriateness of RBC transfusion on a health system level. Electronic health record (EHR) clinical decision support systems have been shown to be effective in encouraging providers to transfuse at appropriate Hb thresholds. We present our experience with an interruptive best practice alert (BPA) at a paediatric healthcare system.

MATERIALS AND METHODS

An interruptive BPA requiring physician response was implemented in our EHR (Epic Systems Corp., Verona, WI, USA) in 2018 based on Hb thresholds for inpatients. The threshold was initially <8 g/dL and later changed to <7 g/dL in 2019. We assessed total activations, number of RBC transfusions and hospital metrics through 2022 compared to the 2 years prior to implementation.

RESULTS

The BPA activated 6956 times over 4 years, slightly less than 5/day, and the success rate, with no RBC transfusions within 24 h of order attempt, was 14.5% (1012/6956). There was a downward trend in the number of total RBC transfusions and RBC transfusions per admission after implementation, non-significant (p = 0.41 and p = >0.99). The annual case mix index was similar over the years evaluated. The estimated cost savings based on acquisition costs for RBC units were 213,822 USD or about $51,891 per year.

CONCLUSION

BPA implementation led to sustained change in RBC transfusion towards best practice, and there were long-term savings in RBC expenditure.

Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach

OBJECTIVES

Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU.

DESIGN

Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework.

SETTING

International survey study.

PATIENT/SUBJECTS

Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support.

INTERVENTIONS

An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions.

MEASUREMENTS AND MAIN RESULTS

A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews.

CONCLUSIONS

In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.

Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.

Virtual Care Team Guided Management of Patients With Heart Failure During Hospitalization

BACKGROUND

Scalable and safe approaches for heart failure guideline-directed medical therapy (GDMT) optimization are needed.

OBJECTIVES

The authors assessed the safety and effectiveness of a virtual care team guided strategy on GDMT optimization in hospitalized patients with heart failure with reduced ejection fraction (HFrEF).

METHODS

In a multicenter implementation trial, we allocated 252 hospital encounters in patients with left ventricular ejection fraction ≤40% to a virtual care team guided strategy (107 encounters among 83 patients) or usual care (145 encounters among 115 patients) across 3 centers in an integrated health system. In the virtual care team group, clinicians received up to 1 daily GDMT optimization suggestion from a physician-pharmacist team. The primary effectiveness outcome was in-hospital change in GDMT optimization score (+2 initiations, +1 dose up-titrations, -1 dose down-titrations, -2 discontinuations summed across classes). In-hospital safety outcomes were adjudicated by an independent clinical events committee.

RESULTS

Among 252 encounters, the mean age was 69 ± 14 years, 85 (34%) were women, 35 (14%) were Black, and 43 (17%) were Hispanic. The virtual care team strategy significantly improved GDMT optimization scores vs usual care (adjusted difference: +1.2; 95% CI: 0.7-1.8; P < 0.001). New initiations (44% vs 23%; absolute difference: +21%; P = 0.001) and net intensifications (44% vs 24%; absolute difference: +20%; P = 0.002) during hospitalization were higher in the virtual care team group, translating to a number needed to intervene of 5 encounters. Overall, 23 (21%) in the virtual care team group and 40 (28%) in usual care experienced 1 or more adverse events (P = 0.30). Acute kidney injury, bradycardia, hypotension, hyperkalemia, and hospital length of stay were similar between groups.

CONCLUSIONS

Among patients hospitalized with HFrEF, a virtual care team guided strategy for GDMT optimization was safe and improved GDMT across multiple hospitals in an integrated health system. Virtual teams represent a centralized and scalable approach to optimize GDMT.

Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study

BACKGROUND

Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria.

OBJECTIVES

Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden.

METHODS

We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient.

RESULTS

In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week.

CONCLUSION

Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.

Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing

BACKGROUND

Pseudorandomized testing can be applied to perform rigorous yet practical evaluations of clinical decision support tools. We apply this methodology to an interruptive alert aimed at reducing free-text prescriptions. Using free-text instead of structured computerized provider order entry elements can cause medication errors and inequity in care by bypassing medication-based clinical decision support tools and hindering automated translation of prescription instructions.

OBJECTIVE

The objective of this study is to evaluate the effectiveness of an interruptive alert at reducing free-text prescriptions via pseudorandomized testing using native electronic health records (EHR) functionality.

METHODS

Two versions of an EHR alert triggered when a provider attempted to sign a discharge free-text prescription. The visible version displayed an interruptive alert to the user, and a silent version triggered in the background, serving as a control. Providers were assigned to the visible and silent arms based on even/odd EHR provider IDs. The proportion of encounters with a free-text prescription was calculated across the groups. Alert trigger rates were compared in process control charts. Free-text prescriptions were analyzed to identify prescribing patterns.

RESULTS

Over the 28-week study period, 143 providers triggered 695 alerts (345 visible and 350 silent). The proportions of encounters with free-text prescriptions were 83% (266/320) and 90% (273/303) in the intervention and control groups, respectively (p = 0.01). For the active alert, median time to action was 31 seconds. Alert trigger rates between groups were similar over time. Ibuprofen, oxycodone, steroid tapers, and oncology-related prescriptions accounted for most free-text prescriptions. A majority of these prescriptions originated from user preference lists.

CONCLUSION

An interruptive alert was associated with a modest reduction in free-text prescriptions. Furthermore, the majority of these prescriptions could have been reproduced using structured order entry fields. Targeting user preference lists shows promise for future intervention.

Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics

BACKGROUND

Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.

OBJECTIVE

This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I).

METHODS

We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption.

RESULTS

The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden.

CONCLUSIONS

EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.

Use of Clinical Decision Support to Improve the Laboratory Evaluation of Monoclonal Gammopathies

OBJECTIVES

There is considerable variation in ordering practices for the initial laboratory evaluation of monoclonal gammopathies (MGs) despite clear society guidelines to include serum free light chain (sFLC) testing. We assessed the ability of a clinical decision support (CDS) alert to improve guideline compliance and analyzed its clinical impact.

METHODS

We designed and deployed a targeted CDS alert to educate and prompt providers to order an sFLC assay when ordering serum protein electrophoresis (SPEP) testing.

RESULTS

The alert was highly effective at increasing the co-ordering of SPEP and sFLC testing. Preimplementation, 62.8% of all SPEP evaluations included sFLC testing, while nearly 90% of evaluations included an sFLC assay postimplementation. In patients with no prior sFLC testing, analysis of sFLC orders prompted by the alert led to the determination that 28.9% (800/2,769) of these patients had an abnormal κ/λ ratio. In 452 of these patients, the sFLC assay provided the only laboratory evidence of a monoclonal protein. Moreover, within this population, there were numerous instances of new diagnoses of multiple myeloma and other MGs.

CONCLUSIONS

The CDS alert increased compliance with society guidelines and improved the diagnostic evaluation of patients with suspected MGs.

Pharmacogenomics of medications given via nonconventional administration routes: a scoping review

Pharmacogenomics (PGx) implementation has become increasingly widespread. One of the most important aspects of this implementation process is the development of appropriate clinical decision support (CDS). Major PGx resources, such as the Clinical Pharmacogenetics Implementation Consortium, provide valuable recommendations for the development of CDS for specific gene–drug pairs but do not specify whether the administration route of a drug is clinically relevant. It is also unknown if PGx alerts for nonorally and non-intravenously administered PGx-relevant medications should be suppressed to reduce alert fatigue. The purpose of this scoping review was to identify studies and their clinical, pharmacokinetic and pharmacodynamic outcomes to better determine if CDS alerts are relevant for nonorally and non-intravenously administered PGx-relevant medications. Although this scoping review identified multiple PGx studies, the results of these studies were inconsistent, and more evidence is needed regarding different routes of medication administration and PGx.

Real-Time User Feedback to Support Clinical Decision Support System Improvement

OBJECTIVES

To improve clinical decision support (CDS) by allowing users to provide real-time feedback when they interact with CDS tools and by creating processes for responding to and acting on this feedback.

METHODS

Two organizations implemented similar real-time feedback tools and processes in their electronic health record and gathered data over a 30-month period. At both sites, users could provide feedback by using Likert feedback links embedded in all end-user facing alerts, with results stored outside the electronic health record, and provide feedback as a comment when they overrode an alert. Both systems are monitored daily by clinical informatics teams.

RESULTS

The two sites received 2,639 Likert feedback comments and 623,270 override comments over a 30-month period. Through four case studies, we describe our use of end-user feedback to rapidly respond to build errors, as well as identifying inaccurate knowledge management, user-interface issues, and unique workflows.

CONCLUSION

Feedback on CDS tools can be solicited in multiple ways, and it contains valuable and actionable suggestions to improve CDS alerts. Additionally, end users appreciate knowing their feedback is being received and may also make other suggestions to improve the electronic health record. Incorporation of end-user feedback into CDS monitoring, evaluation, and remediation is a way to improve CDS.

Appropriateness of Alerts and Physicians’ Responses with a Medication-related Clinical Decision Support System (Preprint)

Background

Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians’ responses.

Objective

This study aimed to understand the CDSS and physicians’ behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians’ responses in a medication-related passive alert system.

Methods

Data on medication-related orders, alerts, and patients’ electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians’ responses for a subset of 382 alert cases and classified them.

Results

Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians’ responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician’s response. In this study, the override rate was 92.9% (n=355).

Conclusions

We evaluated the appropriateness of alerts and physicians’ responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians’ responses.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Prevalence of Sensitive Terms in Clinical Notes: observational study using natural language processing techniques (Preprint)

Background

Objective

Methods

Results

Conclusions

Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System-Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses

BACKGROUND

Comprehensive clinical decision support (CDS) care maps can improve the delivery of care and clinical outcomes. However, they are frequently plagued by usability problems and poor user acceptance.

OBJECTIVE

This study aims to characterize factors influencing successful design and use of comprehensive CDS care maps and identify themes associated with end-user acceptance of a thoracic trauma CDS care map earlier in the process than has traditionally been done. This was a planned adaptive redesign stage of a User Acceptance and System Adaptation Design development and implementation strategy for a CDS care map. This stage was based on a previously developed prototype CDS care map guided by the Unified Theory of Acceptance and Use of Technology.

METHODS

A total of 22 multidisciplinary end users (physicians, advanced practice providers, and nurses) were identified and recruited using snowball sampling. Qualitative interviews were conducted, audio-recorded, and transcribed verbatim. Generation of prespecified codes and the interview guide was informed by the Unified Theory of Acceptance and Use of Technology constructs and investigative team experience. Interviews were blinded and double-coded. Thematic analysis of interview scripts was conducted and yielded descriptive themes about factors influencing the construction and potential use of an acceptable CDS care map.

RESULTS

A total of eight dominant themes were identified: alert fatigue (theme 1), automation (theme 2), redundancy (theme 3), minimalistic design (theme 4), evidence based (theme 5), prevent errors (theme 6), comprehensive across the spectrum of disease (theme 7), and malleability (theme 8). Themes 1 to 4 addressed factors directly affecting end users, and themes 5 to 8 addressed factors affecting patient outcomes. More experienced providers prioritized a system that is easy to use. Nurses prioritized a system that incorporated evidence into decision support. Clinicians across specialties, roles, and ages agreed that the amount of extra work generated should be minimal and that the system should help them administer optimal care efficiently.

CONCLUSIONS

End user feedback reinforces attention toward factors that improve the acceptance and use of a CDS care map for patients with thoracic trauma. Common themes focused on system complexity, the ability of the system to fit different populations and settings, and optimal care provision. Identifying these factors early in the development and implementation process may facilitate user-centered design and improve adoption.

Optimized Use of the Electronic Health Record and Other Clinical Resources to Enhance the Management of Hypogonadal Men

Electronic health records (EHRs) have enabled electronic documentation of a tremendous amount of clinical data. EHRs have the potential to improve communication between patients and their providers, facilitate quality improvement and outcomes research, and reduce medical errors. Conversely, EHRs have also increased clinician burnout, information clutter, and depersonalization of the interactions between patients and their providers. Increasing clinician input into EHR design, providing access to technical help, streamlining of workflow, and the use of custom templates that have fewer requirements for evaluation and management coding can reduce this burnout and increase the utility of this advancing technology.

The potential for leveraging machine learning to filter medication alerts

OBJECTIVE

To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view.

MATERIALS AND METHODS

We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1-there is no difference in precision among prediction models; and H2-the removal of any feature category does not change precision.

RESULTS

A total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (-0.147, P = 0.001).

CONCLUSIONS

Machine learning potentially enables the intelligent filtering of medication alerts.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics

BACKGROUND

Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.

OBJECTIVE

(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.

MATERIALS AND METHODS

We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.

RESULTS

Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.

CONCLUSION

Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

A Time and Motion Analysis of Nursing Workload and Electronic Health Record Use in the Emergency Department

INTRODUCTION

The use of an electronic health record may create unanticipated consequences for emergency care delivery. We sought to describe emergency department nursing task distribution and the use of the electronic health record.

METHODS

This was a prospective observational study of nurses in the emergency department using a time-and-motion methodology. Three trained research assistants conducted 1:1 observations between March and September 2019. Nurse tasks were classified into 6 established categories: electronic health record, direct/indirect patient care, communication, personal time, and other. Nurses' perceived workload was assessed using the National Aeronautics and Space Administration (NASA) Task Load Index.

RESULTS

Twenty-three observations were conducted over 46 hours. Overall, nurses spent 27% of their time on electronic health record tasks, 25% on direct patient care, 17% on personal time, 15% on indirect patient care, and 6% on communication. During morning (7 am-12 pm) and afternoon shifts (12 pm-3 pm), the use of the health record was the most commonly performed task, whereas indirect patient care was the task most performed during evening shifts (3 pm-12 pm). Using the National Aeronautics and Space Administration (NASA) Task Load Index, nurses reported an increase in mental demand and effort during afternoon shifts compared with morning shifts.

DISCUSSION

We observed that emergency nurses spent more time using the electronic health record as compared to other tasks. Increased usability of the electronic health record, particularly during high occupancy periods, may be a target for improvement.

Human Factors and Organizational Issues in Health Informatics: Innovations and Opportunities

OBJECTIVE

Human factors and ergonomics (HF/E) frameworks and methods are becoming embedded in the health informatics community. There is now broad recognition that health informatics tools must account for the diverse needs, characteristics, and abilities of end users, as well as their context of use. The objective of this review is to synthesize the current nature and scope of HF/E integration into the health informatics community.

METHODS

Because the focus of this synthesis is on understanding the current integration of the HF/E and health informatics research communities, we manually reviewed all manuscripts published in primary HF/E and health informatics journals during 2020.

RESULTS

HF/E-focused health informatics studies included in this synthesis focused heavily on EHR customizations, specifically clinical decision support customizations and customized data displays, and on mobile health innovations. While HF/E methods aimed to jointly improve end user safety, performance, and satisfaction, most HF/E-focused health informatics studies measured only end user satisfaction.

CONCLUSION

HF/E-focused health informatics researchers need to identify and communicate methodological standards specific to health informatics, to better synthesize findings across resource intensive HF/E-focused health informatics studies. Important gaps in the HF/E design and evaluation process should be addressed in future work, including support for technology development platforms and training programs so that health informatics designers are as diverse as end users.

Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation

Purpose

The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral.

Methods

In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences.

Results

The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage.

Conclusions

This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral.

Translational Relevance

The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements

OBJECTIVE

Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements.

METHODS

Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors.

RESULTS

Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback.

DISCUSSION

An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations.

CONCLUSION

Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts

Objectives

While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective.

Materials and Methods

We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance.

Results

We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests.

Conclusions

Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.

Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals

OBJECTIVE

To identify and summarize the current internal governance processes adopted by hospitals, as reported in the literature, for selecting, optimizing, and evaluating clinical decision support (CDS) alerts in order to identify effective approaches.

MATERIALS AND METHODS

Databases (Medline, Embase, CINAHL, Scopus, Web of Science, IEEE Xplore Digital Library, CADTH, and WorldCat) were searched to identify relevant papers published from January 2010 to April 2020. All paper types published in English that reported governance processes for selecting and/or optimizing CDS alerts in hospitals were included.

RESULTS

Eight papers were included in the review. Seven papers focused specifically on medication-related CDS alerts. All papers described the use of a multidisciplinary committee to optimize alerts. Other strategies included the use of clinician feedback, alert data, literature and drug references, and a visual dashboard. Six of the 8 papers reported evaluations of their CDS alert modifications following the adoption of optimization strategies, and of these, 5 reported a reduction in alert rate.

CONCLUSIONS

A multidisciplinary committee, often in combination with other approaches, was the most frequent strategy reported by hospitals to optimize their CDS alerts. Due to the limited number of published processes, variation in system changes, and evaluation results, we were unable to compare the effectiveness of different strategies, although employing multiple strategies appears to be an effective approach for reducing CDS alert numbers. We recommend hospitals report on descriptions and evaluations of governance processes to enable identification of effective strategies for optimization of CDS alerts in hospitals.

Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert)

Background

Clinical decision support (CDS) is a tool that helps clinicians in decision making by generating clinical alerts to supplement their previous knowledge and experience. However, CDS generates a high volume of irrelevant alerts, resulting in alert fatigue among clinicians. Alert fatigue is the mental state of alerts consuming too much time and mental energy, which often results in relevant alerts being overridden unjustifiably, along with clinically irrelevant ones. Consequently, clinicians become less responsive to important alerts, which opens the door to medication errors.

Objective

This study aims to explore how a blockchain-based solution can reduce alert fatigue through collaborative alert sharing in the health sector, thus improving overall health care quality for both patients and clinicians.

Methods

We have designed a 4-step approach to answer this research question. First, we identified five potential challenges based on the published literature through a scoping review. Second, a framework is designed to reduce alert fatigue by addressing the identified challenges with different digital components. Third, an evaluation is made by comparing MedAlert with other proposed solutions. Finally, the limitations and future work are also discussed.

Results

Of the 341 academic papers collected, 8 were selected and analyzed. MedAlert securely distributes low-level (nonlife-threatening) clinical alerts to patients, enabling a collaborative clinical decision. Among the solutions in our framework, Hyperledger (private permissioned blockchain) and BankID (federated digital identity management) have been selected to overcome challenges such as data integrity, user identity, and privacy issues.

Conclusions

MedAlert can reduce alert fatigue by attracting the attention of patients and clinicians, instead of solely reducing the total number of alerts. MedAlert offers other advantages, such as ensuring a higher degree of patient privacy and faster transaction times compared with other frameworks. This framework may not be suitable for elderly patients who are not technology savvy or in-patients. Future work in validating this framework based on real health care scenarios is needed to provide the performance evaluations of MedAlert and thus gain support for the better development of this idea.